Overview

Dataset statistics

Number of variables17
Number of observations2130471
Missing cells4357845
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory276.3 MiB
Average record size in memory136.0 B

Variable types

NUM11
CAT5
BOOL1

Warnings

data has a high cardinality: 410 distinct values High cardinality
municipio has a high cardinality: 5297 distinct values High cardinality
nomeRegiaoSaude has a high cardinality: 440 distinct values High cardinality
codmun is highly correlated with coduf and 1 other fieldsHigh correlation
coduf is highly correlated with codmun and 1 other fieldsHigh correlation
codRegiaoSaude is highly correlated with coduf and 1 other fieldsHigh correlation
obitosAcumulado is highly correlated with casosAcumulado and 1 other fieldsHigh correlation
casosAcumulado is highly correlated with obitosAcumulado and 1 other fieldsHigh correlation
obitosNovos is highly correlated with casosNovosHigh correlation
casosNovos is highly correlated with obitosNovosHigh correlation
Recuperadosnovos is highly correlated with casosAcumulado and 1 other fieldsHigh correlation
estado is highly correlated with regiaoHigh correlation
regiao is highly correlated with estadoHigh correlation
Recuperadosnovos has 2130114 (> 99.9%) missing values Missing
emAcompanhamentoNovos has 2130114 (> 99.9%) missing values Missing
populacaoTCU2019 is highly skewed (γ1 = 63.68529126) Skewed
casosAcumulado is highly skewed (γ1 = 98.21588694) Skewed
casosNovos is highly skewed (γ1 = 92.80026898) Skewed
obitosAcumulado is highly skewed (γ1 = 88.56965586) Skewed
obitosNovos is highly skewed (γ1 = 117.250021) Skewed
casosAcumulado has 275101 (12.9%) zeros Zeros
casosNovos has 1265420 (59.4%) zeros Zeros
obitosAcumulado has 691212 (32.4%) zeros Zeros
obitosNovos has 1977578 (92.8%) zeros Zeros

Reproduction

Analysis started2021-04-10 18:44:26.679326
Analysis finished2021-04-10 18:52:00.589797
Duration7 minutes and 33.91 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

data
Categorical

HIGH CARDINALITY

Distinct410
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.3 MiB
2021-01-13
 
5620
2020-12-24
 
5620
2020-12-19
 
5619
2020-07-11
 
5619
2021-01-01
 
5619
Other values (405)
2102374 
ValueCountFrequency (%) 
2021-01-1356200.3%
 
2020-12-2456200.3%
 
2020-12-1956190.3%
 
2020-07-1156190.3%
 
2021-01-0156190.3%
 
2020-10-2356190.3%
 
2021-02-0556190.3%
 
2020-05-0256190.3%
 
2020-06-1856190.3%
 
2020-05-2356190.3%
 
Other values (400)207427997.4%
 
2021-04-10T15:52:01.293907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-10T15:52:01.539758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

regiao
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.3 MiB
Nordeste
687027 
Sudeste
635328 
Sul
453756 
Centro-Oeste
179391 
Norte
174557 
ValueCountFrequency (%) 
Nordeste68702732.2%
 
Sudeste63532829.8%
 
Sul45375621.3%
 
Centro-Oeste1793918.4%
 
Norte1745578.2%
 
Brasil412< 0.1%
 
2021-04-10T15:52:02.038146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-10T15:52:02.276005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:52:02.487045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length7
Mean length6.727493122
Min length3

estado
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing412
Missing (%)< 0.1%
Memory size16.3 MiB
MG
324076 
SP
245244 
RS
189152 
BA
158832 
PR
152010 
Other values (22)
1060745 
ValueCountFrequency (%) 
MG32407615.2%
 
SP24524411.5%
 
RS1891528.9%
 
BA1588327.5%
 
PR1520107.1%
 
SC1125945.3%
 
GO940234.4%
 
PI856854.0%
 
PB853064.0%
 
MA830323.9%
 
Other values (17)60010528.2%
 
2021-04-10T15:52:02.779123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-10T15:52:03.285441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.000193384
Min length2

municipio
Categorical

HIGH CARDINALITY

Distinct5297
Distinct (%)0.3%
Missing19441
Missing (%)0.9%
Memory size16.3 MiB
Bom Jesus
 
1895
São Domingos
 
1895
São Francisco
 
1516
Santa Terezinha
 
1516
Bonito
 
1516
Other values (5292)
2102692 
ValueCountFrequency (%) 
Bom Jesus18950.1%
 
São Domingos18950.1%
 
São Francisco15160.1%
 
Santa Terezinha15160.1%
 
Bonito15160.1%
 
Santa Inês15160.1%
 
Vera Cruz15160.1%
 
Santa Helena15160.1%
 
Planalto15160.1%
 
Santa Luzia15160.1%
 
Other values (5287)209511298.3%
 
(Missing)194410.9%
 
2021-04-10T15:52:03.985083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-10T15:52:04.334617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length10
Mean length11.52917266
Min length3

coduf
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.35978382
Minimum11
Maximum76
Zeros0
Zeros (%)0.0%
Memory size16.3 MiB
2021-04-10T15:52:04.607795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile15
Q125
median31
Q341
95-th percentile51
Maximum76
Range65
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.876236041
Coefficient of variation (CV)0.3052009276
Kurtosis-0.4680371154
Mean32.35978382
Median Absolute Deviation (MAD)7
Skewness0.1587902939
Sum68941581
Variance97.54003833
MonotocityNot monotonic
2021-04-10T15:52:04.833690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
3132407615.2%
 
3524524411.5%
 
431891528.9%
 
291588327.5%
 
411520107.1%
 
421125945.3%
 
52940234.4%
 
22856854.0%
 
25853064.0%
 
21830323.9%
 
Other values (18)60051728.2%
 
ValueCountFrequency (%) 
11204971.0%
 
1287480.4%
 
13239081.1%
 
1464740.3%
 
15549862.6%
 
ValueCountFrequency (%) 
76412< 0.1%
 
53789< 0.1%
 
52940234.4%
 
51542282.5%
 
50303511.4%
 

codmun
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5591
Distinct (%)0.3%
Missing11482
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean325258.0141
Minimum110000
Maximum530010
Zeros0
Zeros (%)0.0%
Memory size16.3 MiB
2021-04-10T15:52:05.093663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum110000
5-th percentile150770
Q1251200
median314610
Q3411915
95-th percentile510730
Maximum530010
Range420010
Interquartile range (IQR)160715

Descriptive statistics

Standard deviation98535.04745
Coefficient of variation (CV)0.3029442571
Kurtosis-0.5267074737
Mean325258.0141
Median Absolute Deviation (MAD)74150
Skewness0.122032906
Sum6.892181541e+11
Variance9709155576
MonotocityNot monotonic
2021-04-10T15:52:05.450566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
412350379< 0.1%
 
350640379< 0.1%
 
292273379< 0.1%
 
313140379< 0.1%
 
210923379< 0.1%
 
410304379< 0.1%
 
270560379< 0.1%
 
251272379< 0.1%
 
314350379< 0.1%
 
353590379< 0.1%
 
Other values (5581)211519999.3%
 
(Missing)114820.5%
 
ValueCountFrequency (%) 
110000379< 0.1%
 
110001379< 0.1%
 
110002379< 0.1%
 
110003379< 0.1%
 
110004379< 0.1%
 
ValueCountFrequency (%) 
530010379< 0.1%
 
522230379< 0.1%
 
522220379< 0.1%
 
522205379< 0.1%
 
522200379< 0.1%
 

codRegiaoSaude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct450
Distinct (%)< 0.1%
Missing19441
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean32403.1237
Minimum11001
Maximum53001
Zeros0
Zeros (%)0.0%
Memory size16.3 MiB
2021-04-10T15:52:05.922054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11001
5-th percentile15012
Q125010
median31059
Q341015
95-th percentile51013
Maximum53001
Range42000
Interquartile range (IQR)16005

Descriptive statistics

Standard deviation9836.343636
Coefficient of variation (CV)0.3035615865
Kurtosis-0.5240045909
Mean32403.1237
Median Absolute Deviation (MAD)7056
Skewness0.1399733425
Sum6.840396622e+10
Variance96753656.13
MonotocityNot monotonic
2021-04-10T15:52:06.233735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22009159180.7%
 
24006140230.7%
 
50001128860.6%
 
43016125070.6%
 
50003125070.6%
 
26003121280.6%
 
31007121280.6%
 
22004117490.6%
 
41015113700.5%
 
42001113700.5%
 
Other values (440)198444493.1%
 
(Missing)194410.9%
 
ValueCountFrequency (%) 
1100134110.2%
 
1100222740.1%
 
1100353060.2%
 
1100418950.1%
 
1100530320.1%
 
ValueCountFrequency (%) 
53001379< 0.1%
 
5201830320.1%
 
5201745480.2%
 
5201637900.2%
 
5201568220.3%
 

nomeRegiaoSaude
Categorical

HIGH CARDINALITY

Distinct440
Distinct (%)< 0.1%
Missing19441
Missing (%)0.9%
Memory size16.3 MiB
CENTRAL
 
21982
SUL
 
16676
VALE DO RIO GUARIBAS
 
15918
6ª REGIAO DE SAUDE - PAU DOS FERROS
 
14023
NORTE
 
13265
Other values (435)
2029166 
ValueCountFrequency (%) 
CENTRAL219821.0%
 
SUL166760.8%
 
VALE DO RIO GUARIBAS159180.7%
 
6ª REGIAO DE SAUDE - PAU DOS FERROS140230.7%
 
NORTE132650.6%
 
CAMPO GRANDE128860.6%
 
REGIAO 16125070.6%
 
DOURADOS125070.6%
 
CARUARU121280.6%
 
POUSO ALEGRE121280.6%
 
Other values (430)196701092.3%
 
(Missing)194410.9%
 
2021-04-10T15:52:06.725712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-10T15:52:07.001345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length42
Median length11
Mean length13.34476836
Min length3

semanaEpi
Real number (ℝ≥0)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.71391209
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size16.3 MiB
2021-04-10T15:52:07.291829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median26
Q340
95-th percentile51
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.25448307
Coefficient of variation (CV)0.5710314167
Kurtosis-1.206169529
Mean26.71391209
Median Absolute Deviation (MAD)13
Skewness0.04191808142
Sum56913215
Variance232.6992536
MonotocityNot monotonic
2021-04-10T15:52:07.618836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14730473.4%
 
13507112.4%
 
10395291.9%
 
12395291.9%
 
11395291.9%
 
9394731.9%
 
52393341.8%
 
2393341.8%
 
39393331.8%
 
38393331.8%
 
Other values (43)169131979.4%
 
ValueCountFrequency (%) 
1393331.8%
 
2393341.8%
 
3393331.8%
 
4393331.8%
 
5393331.8%
 
ValueCountFrequency (%) 
53393331.8%
 
52393341.8%
 
51393331.8%
 
50393331.8%
 
49393331.8%
 

populacaoTCU2019
Real number (ℝ≥0)

SKEWED

Distinct5104
Distinct (%)0.2%
Missing7959
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean118909.4324
Minimum781
Maximum210147125
Zeros0
Zeros (%)0.0%
Memory size16.3 MiB
2021-04-10T15:52:07.926249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum781
5-th percentile2515
Q15474
median11708
Q325768
95-th percentile122859
Maximum210147125
Range210146344
Interquartile range (IQR)20294

Descriptive statistics

Standard deviation3058444.129
Coefficient of variation (CV)25.72078655
Kurtosis4324.374913
Mean118909.4324
Median Absolute Deviation (MAD)7546
Skewness63.68529126
Sum2.523866971e+11
Variance9.354080488e+12
MonotocityNot monotonic
2021-04-10T15:52:08.221220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
523718950.1%
 
320315160.1%
 
764215160.1%
 
380215160.1%
 
457315160.1%
 
1101915160.1%
 
1889511370.1%
 
2521611370.1%
 
478611370.1%
 
534811370.1%
 
Other values (5094)210848999.0%
 
(Missing)79590.4%
 
ValueCountFrequency (%) 
781379< 0.1%
 
837379< 0.1%
 
935379< 0.1%
 
1034379< 0.1%
 
1112379< 0.1%
 
ValueCountFrequency (%) 
210147125412< 0.1%
 
45919049410< 0.1%
 
21168791410< 0.1%
 
17264943410< 0.1%
 
14873064410< 0.1%
 

casosAcumulado
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct35575
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.212752
Minimum0
Maximum13373174
Zeros275101
Zeros (%)12.9%
Memory size16.3 MiB
2021-04-10T15:52:08.514010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median118
Q3464
95-th percentile3103
Maximum13373174
Range13373174
Interquartile range (IQR)450

Descriptive statistics

Standard deviation88095.9064
Coefficient of variation (CV)32.71034058
Kurtosis11168.33667
Mean2693.212752
Median Absolute Deviation (MAD)118
Skewness98.21588694
Sum5737811666
Variance7760888725
MonotocityNot monotonic
2021-04-10T15:52:08.793980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
027510112.9%
 
1568672.7%
 
2343661.6%
 
3253361.2%
 
4211981.0%
 
5194580.9%
 
6166250.8%
 
7144800.7%
 
8134550.6%
 
9122550.6%
 
Other values (35565)164133077.0%
 
ValueCountFrequency (%) 
027510112.9%
 
1568672.7%
 
2343661.6%
 
3253361.2%
 
4211981.0%
 
ValueCountFrequency (%) 
133731741< 0.1%
 
132798571< 0.1%
 
131932051< 0.1%
 
131005801< 0.1%
 
130136011< 0.1%
 

casosNovos
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct3984
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.83129224
Minimum-13915
Maximum100158
Zeros1265420
Zeros (%)59.4%
Memory size16.3 MiB
2021-04-10T15:52:09.079646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-13915
5-th percentile0
Q10
median0
Q32
95-th percentile26
Maximum100158
Range114073
Interquartile range (IQR)2

Descriptive statistics

Standard deviation588.0277341
Coefficient of variation (CV)31.22609573
Kurtosis10364.5316
Mean18.83129224
Median Absolute Deviation (MAD)0
Skewness92.80026898
Sum40119522
Variance345776.6161
MonotocityNot monotonic
2021-04-10T15:52:09.657108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0126542059.4%
 
121548510.1%
 
21124535.3%
 
3744663.5%
 
4543992.6%
 
5423072.0%
 
6334671.6%
 
7272511.3%
 
8234861.1%
 
9196650.9%
 
Other values (3974)26207212.3%
 
ValueCountFrequency (%) 
-139151< 0.1%
 
-79261< 0.1%
 
-36841< 0.1%
 
-29771< 0.1%
 
-25321< 0.1%
 
ValueCountFrequency (%) 
1001581< 0.1%
 
933171< 0.1%
 
926251< 0.1%
 
910971< 0.1%
 
906381< 0.1%
 

obitosAcumulado
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct7785
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.57465884
Minimum0
Maximum348718
Zeros691212
Zeros (%)32.4%
Memory size16.3 MiB
2021-04-10T15:52:11.717021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile71
Maximum348718
Range348718
Interquartile range (IQR)9

Descriptive statistics

Standard deviation2322.288223
Coefficient of variation (CV)31.56369679
Kurtosis9235.907096
Mean73.57465884
Median Absolute Deviation (MAD)2
Skewness88.56965586
Sum156748677
Variance5393022.59
MonotocityNot monotonic
2021-04-10T15:52:11.981996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
069121232.4%
 
125088611.8%
 
21689147.9%
 
31235105.8%
 
4984724.6%
 
5766083.6%
 
6651483.1%
 
7532182.5%
 
8429392.0%
 
9373471.8%
 
Other values (7775)52221724.5%
 
ValueCountFrequency (%) 
069121232.4%
 
125088611.8%
 
21689147.9%
 
31235105.8%
 
4984724.6%
 
ValueCountFrequency (%) 
3487181< 0.1%
 
3450251< 0.1%
 
3407761< 0.1%
 
3369471< 0.1%
 
3327521< 0.1%
 

obitosNovos
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct692
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4910435298
Minimum-292
Maximum4249
Zeros1977578
Zeros (%)92.8%
Memory size16.3 MiB
2021-04-10T15:52:12.266589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-292
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4249
Range4541
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.36046199
Coefficient of variation (CV)33.31774273
Kurtosis19853.74739
Mean0.4910435298
Median Absolute Deviation (MAD)0
Skewness117.250021
Sum1046154
Variance267.6647166
MonotocityNot monotonic
2021-04-10T15:52:12.526565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0197757892.8%
 
1926904.4%
 
2210441.0%
 
389290.4%
 
448780.2%
 
-137200.2%
 
533380.2%
 
622690.1%
 
716690.1%
 
813100.1%
 
Other values (682)130460.6%
 
ValueCountFrequency (%) 
-2921< 0.1%
 
-2381< 0.1%
 
-2211< 0.1%
 
-1111< 0.1%
 
-751< 0.1%
 
ValueCountFrequency (%) 
42491< 0.1%
 
41951< 0.1%
 
38691< 0.1%
 
38291< 0.1%
 
37801< 0.1%
 

Recuperadosnovos
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct357
Distinct (%)100.0%
Missing2130114
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean4587084.748
Minimum22130
Maximum11791885
Zeros0
Zeros (%)0.0%
Memory size16.3 MiB
2021-04-10T15:52:12.788177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum22130
5-th percentile54554
Q11321036
median4568813
Q37167651
95-th percentile10526727.6
Maximum11791885
Range11769755
Interquartile range (IQR)5846615

Descriptive statistics

Standard deviation3416743.181
Coefficient of variation (CV)0.7448615773
Kurtosis-1.014059367
Mean4587084.748
Median Absolute Deviation (MAD)2934539
Skewness0.2887360224
Sum1637589255
Variance1.167413396e+13
MonotocityNot monotonic
2021-04-10T15:52:13.058150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
77096021< 0.1%
 
44701631< 0.1%
 
36711281< 0.1%
 
63549721< 0.1%
 
51895291< 0.1%
 
93236961< 0.1%
 
106016581< 0.1%
 
71440111< 0.1%
 
92810181< 0.1%
 
3253951< 0.1%
 
Other values (347)347< 0.1%
 
(Missing)2130114> 99.9%
 
ValueCountFrequency (%) 
221301< 0.1%
 
229911< 0.1%
 
243251< 0.1%
 
253181< 0.1%
 
265731< 0.1%
 
ValueCountFrequency (%) 
117918851< 0.1%
 
117321931< 0.1%
 
116641581< 0.1%
 
115587841< 0.1%
 
114361891< 0.1%
 

emAcompanhamentoNovos
Real number (ℝ≥0)

MISSING

Distinct357
Distinct (%)100.0%
Missing2130114
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean622927.0588
Minimum14062
Maximum1317658
Zeros0
Zeros (%)0.0%
Memory size16.3 MiB
2021-04-10T15:52:13.545535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14062
5-th percentile69550.4
Q1427526
median642131
Q3794182
95-th percentile1209815.2
Maximum1317658
Range1303596
Interquartile range (IQR)366656

Descriptive statistics

Standard deviation297312.6697
Coefficient of variation (CV)0.4772832798
Kurtosis-0.06565441078
Mean622927.0588
Median Absolute Deviation (MAD)179567
Skewness0.06726410252
Sum222384960
Variance8.839482356e+10
MonotocityNot monotonic
2021-04-10T15:52:13.831136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4649231< 0.1%
 
7050201< 0.1%
 
3259571< 0.1%
 
8252321< 0.1%
 
488721< 0.1%
 
8040351< 0.1%
 
5637821< 0.1%
 
5406921< 0.1%
 
8959191< 0.1%
 
12960021< 0.1%
 
Other values (347)347< 0.1%
 
(Missing)2130114> 99.9%
 
ValueCountFrequency (%) 
140621< 0.1%
 
150151< 0.1%
 
160131< 0.1%
 
175331< 0.1%
 
196061< 0.1%
 
ValueCountFrequency (%) 
13176581< 0.1%
 
13095411< 0.1%
 
13052481< 0.1%
 
13001851< 0.1%
 
12960021< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing19441
Missing (%)0.9%
Memory size16.3 MiB
0
1964736 
1
 
146294
(Missing)
 
19441
ValueCountFrequency (%) 
0196473692.2%
 
11462946.9%
 
(Missing)194410.9%
 
2021-04-10T15:52:14.215313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2021-04-10T15:49:52.221202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:49:56.903635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:49:57.178654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:49:57.481619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:05.936166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:06.227640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:06.548988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:06.914787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:07.216794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:07.594930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:08.031291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:08.311185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:08.476169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:08.636883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:08.811864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:08.971849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:09.236190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:09.471161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:09.646538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:09.836070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:09.996053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:10.191581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:10.416558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:10.621541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:10.841854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:11.121822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:11.331266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:11.561243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:11.779329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:11.997357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:12.299927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:12.487702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:12.657684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:12.829406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:12.999386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:13.641554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:14.034338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:14.465650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:14.745180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:14.971746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:15.216720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:15.486472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:15.685503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:15.965472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:16.253097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:16.488074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:16.702815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:16.928203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:17.141508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:17.371485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:17.546468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:17.783020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:18.017993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:18.228368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:18.468343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:18.816296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:19.104130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:19.350299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:19.645366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:20.051651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:20.478307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:20.914106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:21.326750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:21.552349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:21.788925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:22.044938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:22.259914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:22.540825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:22.758837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:22.987138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:23.350285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:23.607179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:23.847157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:24.083808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:24.524614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:24.699600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:24.859583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:25.037416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:25.325521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:25.606079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:25.843126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:26.045537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:26.284531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:26.647630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:26.978971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:27.399976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:27.749335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:28.001346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:28.280996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:28.515974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:28.760461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:29.020436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:29.311144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:29.581120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:29.829082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:30.074057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:30.371580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:30.621556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:30.904233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:31.179205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:31.465034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:31.730012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:32.018688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:32.303659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:32.801130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:33.305592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:33.887362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:34.562613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:35.103000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:35.712714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:36.207422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:36.612381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:37.033476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:37.562092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:37.990102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:38.298913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:38.563751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:38.926525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:39.191362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:39.689055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:50:40.254091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-10T15:52:14.398651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-10T15:52:14.808280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-10T15:52:15.208236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-10T15:52:15.985737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-10T15:52:16.773293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-10T15:50:50.269964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:51:00.726847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:51:53.645541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-10T15:51:55.726568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

dataregiaoestadomunicipiocodufcodmuncodRegiaoSaudenomeRegiaoSaudesemanaEpipopulacaoTCU2019casosAcumuladocasosNovosobitosAcumuladoobitosNovosRecuperadosnovosemAcompanhamentoNovosinterior/metropolitana
02020-02-25BrasilNaNNaN76NaNNaNNaN9210147125.00000NaNNaNNaN
12020-02-26BrasilNaNNaN76NaNNaNNaN9210147125.01100NaNNaNNaN
22020-02-27BrasilNaNNaN76NaNNaNNaN9210147125.01000NaNNaNNaN
32020-02-28BrasilNaNNaN76NaNNaNNaN9210147125.01000NaNNaNNaN
42020-02-29BrasilNaNNaN76NaNNaNNaN9210147125.02100NaNNaNNaN
52020-03-01BrasilNaNNaN76NaNNaNNaN10210147125.02000NaNNaNNaN
62020-03-02BrasilNaNNaN76NaNNaNNaN10210147125.02000NaNNaNNaN
72020-03-03BrasilNaNNaN76NaNNaNNaN10210147125.02000NaNNaNNaN
82020-03-04BrasilNaNNaN76NaNNaNNaN10210147125.03100NaNNaNNaN
92020-03-05BrasilNaNNaN76NaNNaNNaN10210147125.07400NaNNaNNaN

Last rows

dataregiaoestadomunicipiocodufcodmuncodRegiaoSaudenomeRegiaoSaudesemanaEpipopulacaoTCU2019casosAcumuladocasosNovosobitosAcumuladoobitosNovosRecuperadosnovosemAcompanhamentoNovosinterior/metropolitana
21304612021-03-31Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL133015268.034436412536029117NaNNaN1.0
21304622021-04-01Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL133015268.034568213186150121NaNNaN1.0
21304632021-04-02Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL133015268.03468731191620757NaNNaN1.0
21304642021-04-03Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL133015268.03486871814623528NaNNaN1.0
21304652021-04-04Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL143015268.03497751088628853NaNNaN1.0
21304662021-04-05Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL143015268.03511631388636678NaNNaN1.0
21304672021-04-06Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL143015268.0352067904644983NaNNaN1.0
21304682021-04-07Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL143015268.03532061139653283NaNNaN1.0
21304692021-04-08Centro-OesteDFBrasília53530010.053001.0DISTRITO FEDERAL143015268.03548161610660977NaNNaN1.0
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